The topics below are organized according to the HOML, but the level of understanding I expect is better reflected in the notes covered in class than the textbook (which is much more in depth).

## Chapter 1 - The Machine Learning Landscape
- Supervised vs Un-supervised
- Regression vs Classification
- ML tasks, applications
- Training and testing
- Parameters and hyper-parameters

## Chapter 4 - Training Models
- Linear regression
- The modeling process (sequence of steps)
- Features and target
- Model parameters
- Predictions and residuals
- Loss and Cost function
- Assessing linear models
- Correlation and co-linearity
- Bias and variance
- Over- and under-fitting 
- Regularization
- Lasso, Ridge, Elastic Net
- Polynomial Regression
- Feature scaling and engineering